People and Faculty

Byron Marshall

Associate Professor

BIS

Byron Marshall

541-737-6054

byron.marshall@bus.oregonstate.edu

People and Faculty

Byron Marshall

Overview

Background

Publications

Overview

Career Interests

Byron's research interests include information security and the re-use of organizational data in informal node-link knowledge representations to support analysis tasks. Previous work includes applications in bioinformatics, business intelligence, digital library, law enforcement, and education. He received a Ph.D. in Management Information Systems from the University of Arizona in May, 2005; an MBA degree with emphasis in Accounting from California State University, Fresno in 1995; and a BA in Business Administration-Computer Applications and Systems from California State University, Fresno in 1988. Byron has 13 years of dynamic industry experience designing, creating, and using computer systems in the cotton industry.

Background

Education

2001-2005: University of Arizona, MIS Department, Tucson, AZ PhD - Management Spring 2005 Major: Management Information Systems, Minor: Linguistics Advisor: Dr. Hsinchun Chen

1995: California State University, Fresno Fresno, CA MBA – emphasis in accounting Graduated with distinction

1988: California State University Fresno Fresno, CA BS – Business Administration – Computer Applications and Systems

Experience

Byron has 13 years of dynamic industry experience designing, creating, and using computer systems in the cotton industry.

Professional Affiliations

Member: AIS, IEEE, ACM, ISACA Academic Advocate

Associate Editor: Journal of Electronic Commerce Research

Publications

Academic Journal

BIS

“Visualizing Basic Accounting Flows: Does XBRL + Model + Animation = Understanding?”

The usefulness of XBRL (eXtensible Business Reporting Language) in facilitating efficient data sharing is clear, but widespread use of XBRL also promises to support more effective analysis processes. This format should allow managers, investors, regulators, and students to aggregate, compare and analyze financial information. This study explores an XBRL-based visualization tool that maps the organization of financial statements captured in the XBRL formalism into a graphical representation that organizes, depicts, and animates financial data. We show that our tool integrates and presents profitability, liquidity, financing, and market value data in a manner recognizable to business students. Our findings suggest the promise of XBRL-based visualization tools both in helping students grasp basic accounting concepts and in facilitating financial analysis in general.

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Conference

BIS

“Visualizing basic accounting flows: does XBRL + model + animation = understanding?”

The usefulness of XBRL (eXtensible Business Reporting Language) in facilitating efficient data sharing is clear, but widespread use of XBRL also promises to support more effective analysis processes. Representing traditional financial statements in this electronic and interoperable format should allow managers, investors, regulators, and importantly students to aggregate, compare and analyze financial information. Processing such data requires an understanding of the underlying paradigms embedded in consolidated sets of financial statements. This work explores the feasibility and effectiveness of an XBRL-based visualization tool, presenting an organizational framework, mapping that framework to financial statements and the XBRL formalism, and demonstrating a visual representation that organizes, depicts, and animates financial data. We show that our tool integrates and presents profitability, liquidity, financing, and market value data in a manner recognizable to business students in introductory financial accounting classes. This preliminary finding suggests the promise of XBRL-based visualization tools both in helping students grasp basic accounting concepts and in facilitating financial analysis in general.

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Conference

BIS

“Dimensional Standard Alignment in K-12 Digital Libraries: Assessment of Self-found vs. Recommended Curriculum”

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Academic Journal

BIS

“Topological Analysis of Criminal Activity Networks: Enhancing Transportation Security”

The security of border and transportation systems is a critical component of the national strategy for homeland security. The security concerns at the border are not independent of law enforcement in border-area jurisdictions because the information known by local law enforcement agencies may provide valuable leads that are useful for securing the border and transportation infrastructure. The combined analysis of law enforcement information and data generated by vehicle license plate readers at international borders can be used to identify suspicious vehicles and people at ports of entry. This not only generates better quality leads for border protection agents but may also serve to reduce wait times for commerce, vehicles, and people as they cross the border. This paper explores the use of criminal activity networks (CANs) to analyze information from law enforcement and other sources to provide value for transportation and border security. We analyze the topological characteristics of CAN of individuals and vehicles in a multiple jurisdiction scenario. The advantages of exploring the relationships of individuals and vehicles are shown. We find that large narcotic networks are small world with short average path lengths ranging from 4.5 to 8.5 and have scale-free degree distributions with power law exponents of 0.85–1.3. In addition, we find that utilizing information from multiple jurisdictions provides higher quality leads by reducing the average shortest-path lengths. The inclusion of vehicular relationships and border-crossing information generates more investigative leads that can aid in securing the border and transportation infrastructure.

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Conference

BIS

“Delivering Value Beyond Efficiency with Visualized XBRL”

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Academic Journal

BIS

“Using Importance Flooding to Identify Interesting Networks of Criminal Activity”

Cross-jurisdictional law enforcement data sharing and analysis is of vital importance because law breakers regularly operate in multiple jurisdictions. Agencies continue to invest massive resources in various sharing initiatives despite several high-profile failures. Key difficulties include: privacy concerns, administrative issues, differences in data representation, and a need for better analysis tools. This work presents a methodology for sharing and analyzing investigation-relevant data and is potentially useful across large cross-jurisdictional data sets. The approach promises to allow crime analysts to use their time more effectively when creating link charts and performing similar analysis tasks. Many potential privacy and security pitfalls are avoided by reducing shared data requirements to labeled relationships between entities. Our importance flooding algorithm helps extract interesting networks of relationships from existing law enforcement records using user-controlled investigation heuristics, spreading activation, and path-based interestingness rules. In our experiments, several variations of the importance flooding approach outperformed relationship-weight-only methods in matching expert-selected associations. We find that accuracy in not substantially affected by reasonable variations in algorithm parameters and demonstrate that user feedback and additional, case-specific information can be usefully added to the computational model.

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Academic Journal

BIS

“User-Centered Evaluation of Arizona BioPathway: An Information Extraction, Integration, and Visualization System”

Explosive growth in biomedical research has made automated information extraction, knowledge integration, and visualization increasingly important and critically needed. The Arizona BioPathway (ABP) system extracts and displays biological regulatory pathway information from the abstracts of journal articles. This study uses relations extracted from more than 200 PubMed abstracts presented in a tabular and graphical user interface with built-in search and aggregation functionality. This article presents a task-centered assessment of the usefulness and usability of the ABP system focusing on its relation aggregation and visualization functionalities. Results suggest that our graph-based visualization is more efficient in supporting pathway analysis tasks and is perceived as more useful and easier to use as compared to a text-based literature viewing method. Relation aggregation significantly contributes to knowledge acquisition efficiency. Together, the graphic and tabular views in the ABP Visualizer provide a flexible and effective interface for pathway relation browsing and analysis. Our study contributes to pathway-related research and biological information extraction by assessing the value of a multi-view, relation-based interface which supports user-controlled exploration of pathway information across multiple granularities.

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Conference

BIS

“Semantics or Standards for Curriculum Search?”

Aligning digital library resources with national and state educational standards to help K-12 teachers search for relevant curriculum is an important issue in the digital library community. Aligning standards from different states promises to help teachers in one state find appropriate materials created and cataloged elsewhere. Although such alignments provide a powerful means for crosswalking standards and curriculum across states, alignment matrices are intrinsically sparse. Hence, we hypothesize that such sparseness may cause significant numbers of false negatives when used for searching curriculum. Our preliminary results confirm the false negative hypothesis, demonstrate the usefulness of term-based techniques in addressing the false negative problem, and explore ways to combine term occurrence data with standards correlations.

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Academic Journal

BIS

“Matching Knowledge Elements in Concept Maps Using a Similarity Flooding Algorithm”

Concept mapping systems used in education and knowledge management emphasize flexibility of representation to enhance learning and facilitate knowledge capture. Collections of concept maps exhibit terminology variance, informality, and organizational variation. These factors make it difficult to match elements between maps in comparison, retrieval, and merging processes. In this work, we add an element anchoring mechanism to a similarity flooding (SF) algorithm to match nodes and substructures between pairs of simulated maps and student-drawn concept maps. Experimental results show significant improvement over simple string matching with combined recall accuracy of 91% for conceptual nodes and concept ¨ link ¨ concept propositions in student-drawn maps.

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Academic Journal

BIS

“Aggregating Automatically Extracted Regulatory Pathway Relations”

Automatic tools to extract information from biomedical texts are needed to help researchers leverage the vast and increasing body of biomedical literature. While several biomedical relation extraction systems have been created and tested, little work has been done to meaningfully organize the extracted relations. Organizational processes should consolidate multiple references to the same objects over various levels of granularity, connect those references to other resources, and capture contextual information. We propose a feature decomposition approach to relation aggregation to support a five-level aggregation framework. Our BioAggregate tagger uses this approach to identify key features in extracted relation name strings. We show encouraging feature assignment accuracy and report substantial consolidation in a network of extracted relations.

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Academic Journal

BIS

“Moving Digital Libraries into the Student Learning Space: the GetSmart Experience”

The GetSmart system was built to support theoretically sound learning processes in a digital library environment by integrating course management, digital library, and concept mapping components to support a constructivist, six-step, information search process. In the fall of 2002 more than 100 students created 1400 concept maps as part of selected computing classes offered at the University of Arizona and Virginia Tech. Those students conducted searches, obtained course information, created concept maps, collaborated in acquiring knowledge, and presented their knowledge representations. This article connects the design elements of the GetSmart system to targeted concept-map-based learning processes, describes our system and research testbed, and analyzes our system usage logs. Results suggest that students did in fact use the tools in an integrated fashion, combining knowledge representation and search activities. After concept mapping was included in the curriculum, we observed improvement in students' online quiz scores. Further, we observed that students in groups collaboratively constructed concept maps with multiple group members viewing and updating map details.

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Conference

BIS

“Linking Ontological Resources Using Aggregatable Substance Identifiers to Organize Extracted Relations”

Systems that extract biological regulatory pathway relations from free-text sources are
intended to help researchers leverage vast and growing collections of research literature.
Several systems to extract such relations have been developed but little work has focused on
how those relations can be usefully organized (aggregated) to support visualization systems or
analysis algorithms. Ontological resources that enumerate name strings for different types of
biomedical objects should play a key role in the organization process. In this paper we
delineate five potentially useful levels of relational granularity and propose the use of
aggregatable substance identifiers to help reduce lexical ambiguity. An aggregatable
substance identifier applies to a gene and its products. We merged 4 extensive lexicons and
compared the extracted strings to the text of five million MEDLINE abstracts. We report on
the ambiguity within and between name strings and common English words. Our results show
an 89% reduction in ambiguity for the extracted human substance name strings when using an
aggregatable substance approach.

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